mean_pinball_loss#

sklearn.metrics.mean_pinball_loss(y_true, y_pred, *, sample_weight=None, alpha=0.5, multioutput='uniform_average')[source]#

分位数回归的 Pinball loss。

Read more in the User Guide.

参数:
y_true形状为 (n_samples,) 或 (n_samples, n_outputs) 的类数组对象

真实（正确）的目标值。

y_pred形状为 (n_samples,) 或 (n_samples, n_outputs) 的类数组对象

估计的目标值。

sample_weightshape 为 (n_samples,) 的 array-like, default=None

样本权重。

alphafloat, slope of the pinball loss, default=0.5,

This loss is equivalent to Mean absolute error when alpha=0.5, alpha=0.95 is minimized by estimators of the 95th percentile.

multioutput{‘raw_values’, ‘uniform_average’} 或形状为 (n_outputs,) 的类数组对象, default=’uniform_average’

定义了多输出值聚合的方式。类数组值定义了用于平均误差的权重。

‘raw_values’

在多输出输入情况下返回完整的误差集。

‘uniform_average’

所有输出的误差以统一权重进行平均。

返回:
lossfloat or ndarray of floats

If multioutput is ‘raw_values’, then mean absolute error is returned for each output separately. If multioutput is ‘uniform_average’ or an ndarray of weights, then the weighted average of all output errors is returned.

The pinball loss output is a non-negative floating point. The best value is 0.0.

示例

>>> from sklearn.metrics import mean_pinball_loss
>>> y_true = [1, 2, 3]
>>> mean_pinball_loss(y_true, [0, 2, 3], alpha=0.1)
0.03...
>>> mean_pinball_loss(y_true, [1, 2, 4], alpha=0.1)
0.3...
>>> mean_pinball_loss(y_true, [0, 2, 3], alpha=0.9)
0.3...
>>> mean_pinball_loss(y_true, [1, 2, 4], alpha=0.9)
0.03...
>>> mean_pinball_loss(y_true, y_true, alpha=0.1)
0.0
>>> mean_pinball_loss(y_true, y_true, alpha=0.9)
0.0